Global Registration of Kinect Point Clouds using Augmented Extended Information Filter and Multiple Features
Z. Kang
a,
, M. Chang
a a
School of Land Science and Technology, China University of Geosciences, 29 Xueyuan Road, Haidian District, Beijing 100083, China - zzkangcugb.edu.cn, charmingxz163.com
Commission VI
， WG IV7
KEY WORDS: Global registration; Kinect; Augmented extended Information filter; Point cloud fitting; Indoor mapping ABSTRACT:
Because the Infra-Red IR Kinect sensor only provides accurate depths up to 5 m for a limited field of view 60
, the problem of registration error accumulation becomes inevitable in indoor mapping. Therefore, in this paper, a global registration method is proposed
based on augmented extended Information Filter AEIF. The point cloud registration is regarded as a stochastic system so that AEIF is used to produces the accurate estimates of rigid transformation parameters through eliminating the error accumulation suffered by
the pair-wise registration. Moreover, because the indoor scene normally contains planar primitives, they can be employed to control the registration of multiple scans. Therefore, the planar primitives are first fitted based on optimized BaySAC algorithm and
simplification algorithm preserving the feature points. Besides the constraint of corresponding points, we then derive the plane normal vector constraint as an additional observation model of AEIF to optimize the registration parameters between each pair of adjacent
scans. The proposed approach is tested on point clouds acquired by a Kinect camera from an indoor environment. The experimental results show that our proposed algorithm is proven to be capable of improving the accuracy of multiple scans aligning by 90.
1. INTRODUCTION
3D scene modeling for indoor environments has stirred significant interest in the last few years. Recently, the Microsoft
Kinect sensors, originally developed as a gaming interface, have received a great deal of attention as being able to produce high
quality depth maps. These RGB-D cameras capture visual data along with per-pixel depth information in real time. However,
one of the biggest problems encountered while processing the Kinect point clouds is the registration in which rigid
transformation parameters RTPs are determined in order to bring one dataset into alignment with the other. Because the
Infra-Red IR Kinect sensor provides accurate depths only up to a limited distance typically less than 5 m for a limited field of
view 60
, the problem of registration error accumulation becomes inevitable when multiple scans registration is required.
Global registration has been a well-discussed issue in terrestrial laser scanning data processing. Bergevin et al. 1996 presented
an algorithm that considers the network of views as a whole and minimises the registration errors of all views simultaneously.
Stoddart and Hilton 1996 identify pair-wise correspondences between points in all views and then iteratively minimise
correspondence errors over all views using a descent algorithm. This basic technique was extended by Neugebauer 1997 and
Eggert et al. 1998 using a multiresolution framework, surface normal processing, and boundary point processing. Williams and
Bennamoun 2000 suggested a further refinement by including individual covariance weights for each point. There is currently
no consensus as to the best approach for solving the global registration problem. Kang et al. 2009 proposed a global
registration method which minimizes the self-closure errors across all scans through simultaneous least-squares adjustment.
Majdi et al.2013 realized a spatial alignment of consecutive 3D data under the supervision of a parallel loop-closure detection
thread. In recent years, the optimization algorithms using a series of
measurements observed over time have been introduced in point cloud registration. Ma and Ellis2004 proposed the Unscented
Particle Filter UPF algorithm to register two point data sets in the presence of isotropic Gaussian noise. In this paper, we regard
the point cloud registration as a stochastic system and the global registration as the process that recursively estimates the rigid
transformation parameters of each scans, so that augmented extended Information Filter AEIF is utilized to produces the
accurate estimates of rigid transformation parameters through eliminating the error accumulation suffered by the pair-wise
registration.
2. GLOBAL REGISTRATION USING AUGMENTED
